Cargando…

A Spatial‐Temporal Model for Event Detection in Social Media

Nowadays, the interest in data modelling from the spatial-temporal perspective is constantly increasing. Moreover, a wide variety of applications, such as social network data, need to be done to study spatiotemporal patterns. In general, however, these patterns are highly complex and challenging, so...

Descripción completa

Detalles Bibliográficos
Autores principales: Boghiu, Șerban, Gîfu, Daniela
Formato: Online Artículo Texto
Lenguaje:English
Publicado: The Author(s). Published by Elsevier B.V. 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7531980/
https://www.ncbi.nlm.nih.gov/pubmed/33042295
http://dx.doi.org/10.1016/j.procs.2020.08.056
_version_ 1783589831704576000
author Boghiu, Șerban
Gîfu, Daniela
author_facet Boghiu, Șerban
Gîfu, Daniela
author_sort Boghiu, Șerban
collection PubMed
description Nowadays, the interest in data modelling from the spatial-temporal perspective is constantly increasing. Moreover, a wide variety of applications, such as social network data, need to be done to study spatiotemporal patterns. In general, however, these patterns are highly complex and challenging, so it is a demanding process to analyze or to classify them as the conventional context in various types of event data. In order to analyze the traffic viral within the text from the perspective of impressive negative effects, we should spatial-temporally localize the event and geographical regions and give a semantically interpreting of what happened. We propose a review of the best models and techniques applied for social media data processing to formalize a novel theory of action and time. This investigation intends to draw the basic knowledge level over which research intended to decipher in texts the occurrence of events, together with their involved characters, and their relationship with time and space.
format Online
Article
Text
id pubmed-7531980
institution National Center for Biotechnology Information
language English
publishDate 2020
publisher The Author(s). Published by Elsevier B.V.
record_format MEDLINE/PubMed
spelling pubmed-75319802020-10-05 A Spatial‐Temporal Model for Event Detection in Social Media Boghiu, Șerban Gîfu, Daniela Procedia Comput Sci Article Nowadays, the interest in data modelling from the spatial-temporal perspective is constantly increasing. Moreover, a wide variety of applications, such as social network data, need to be done to study spatiotemporal patterns. In general, however, these patterns are highly complex and challenging, so it is a demanding process to analyze or to classify them as the conventional context in various types of event data. In order to analyze the traffic viral within the text from the perspective of impressive negative effects, we should spatial-temporally localize the event and geographical regions and give a semantically interpreting of what happened. We propose a review of the best models and techniques applied for social media data processing to formalize a novel theory of action and time. This investigation intends to draw the basic knowledge level over which research intended to decipher in texts the occurrence of events, together with their involved characters, and their relationship with time and space. The Author(s). Published by Elsevier B.V. 2020 2020-10-02 /pmc/articles/PMC7531980/ /pubmed/33042295 http://dx.doi.org/10.1016/j.procs.2020.08.056 Text en © 2020 The Author(s). Published by Elsevier B.V. Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active.
spellingShingle Article
Boghiu, Șerban
Gîfu, Daniela
A Spatial‐Temporal Model for Event Detection in Social Media
title A Spatial‐Temporal Model for Event Detection in Social Media
title_full A Spatial‐Temporal Model for Event Detection in Social Media
title_fullStr A Spatial‐Temporal Model for Event Detection in Social Media
title_full_unstemmed A Spatial‐Temporal Model for Event Detection in Social Media
title_short A Spatial‐Temporal Model for Event Detection in Social Media
title_sort spatial‐temporal model for event detection in social media
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7531980/
https://www.ncbi.nlm.nih.gov/pubmed/33042295
http://dx.doi.org/10.1016/j.procs.2020.08.056
work_keys_str_mv AT boghiuserban aspatialtemporalmodelforeventdetectioninsocialmedia
AT gifudaniela aspatialtemporalmodelforeventdetectioninsocialmedia
AT boghiuserban spatialtemporalmodelforeventdetectioninsocialmedia
AT gifudaniela spatialtemporalmodelforeventdetectioninsocialmedia